RESEARCH ARTICLE Metabolic Correlates of Dopaminergic Loss in Dementia With Lewy Bodies Maria Huber,1 Leonie Beyer, MD,1 Catharina Prix, MD,2 Sonja Schönecker, MD,2 Carla Palleis, MD,2 Boris-Stephan Rauchmann, MD,3,4 Silvia Morbelli, MD, PhD,5,6 Andrea Chincarini, PhD,7 Rose Bruffaerts, MD, PhD,8,9 Rik Vandenberghe, MD,8,9 Koen Van Laere, MD,10 Milica G. Kramberger, MD, PhD,11 Maja Trost, MD,11,12 Marko Grmek, MD,12 Valentina Garibotto, MD,13 Nicolas Nicastro, MD,14,15 Giovanni B. Frisoni, MD,16 Afina W. Lemstra, MD, PhD,17 Jessica van der Zande, MSc,17 Andrea Pilotto, MD,18,19 Alessandro Padovani, MD, PhD,18 Sara Garcia-Ptacek, MD, PhD,20,21 Irina Savitcheva, MD, PhD,22 Miguel A. Ochoa-Figueroa, MD, PhD,23,24,25 Anette Davidsson, PhD,23 Valle Camacho, MD, PhD,26 Enrico Peira,7,27 Dario Arnaldi, MD, PhD,5,27 Matteo Bauckneht, MD,5,6 Matteo Pardini, PhD,5,27 Gianmario Sambuceti, MD,5,6 Jonathan Vöglein, MD,2,28 Jonas Schnabel,1 Marcus Unterrainer, MD,1 Robert Perneczky, MD,3,28,29,30 Oliver Pogarell, MD,3 Katharina Buerger, MD,28,30 Cihan Catak, MD,30 Peter Bartenstein, MD,1,31 Paul Cumming, PhD,32,33 Michael Ewers, MD,28 Adrian Danek, MD,2 Johannes Levin, MD,2,28,31 Dag Aarsland, MD,34,35 Flavio Nobili, MD,5,27 Axel Rominger, MD,1,31,32 and Matthias Brendel, MD1,31* 1 Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany 2 Department of Neurology, University Hospital of Munich, LMU Munich, Munich, Germany 3 Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany 4 Department of Radiology, University Hospital of Munich, LMU Munich, Munich, Germany 5 IRCCS Ospedale Policlinico San Martino, Genoa, Italy 6 Nuclear Medicine Unit, Department of Health Sciences, University of Genoa, Genoa, Italy 7 National Institute of Nuclear Physics (INFN), Genoa section, Genoa, Genoa, Italy 8 Department of Neurosciences, Faculty of Medicine, KU Leuven, Leuven, Belgium 9 Department of Neurology, University Hospitals Leuven, Leuven, Belgium 10 Department of Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium 11 Department of Neurology, University Medical Centre, Ljubljana, Slovenia 12 Department for Nuclear Medicine, University Medical Centre, Ljubljana, Slovenia 13 Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals and NIMTLab, Geneva University, Geneva, Switzerland 14 Department of Clinical Neurosciences, Geneva University Hospitals, Geneva, Switzerland 15 Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom 16 LANVIE (Laboratoire de Neuroimagerie du Vieillissement), Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland 17 VU Medical Center Alzheimer Center, Amsterdam, The Netherlands 18 Neurology Unit, University of Brescia, Brescia, Italy 19 Parkinson’s Disease Rehabilitation Centre, FERB ONLUS–S. Isidoro Hospital, Trescore Balneario (BG), Italy 20 Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden 21 Internal Medicine, section for Neurology, Sädersjukhuset, Stockholm, Sweden 22 Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden 23 Department of Clinical Physiology, Institution of Medicine and Health Sciences, Linköping University Hospital, Linköping, Sweden 24 Department of Diagnostic Radiology, Linköping University Hospital, Linköping, Sweden 25 Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden 26 Servicio de Medicina Nuclear, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, España 27 Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy 28 DZNE–German Center for Neurodegenerative Diseases, Munich, Germany 29 Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College, London, United Kingdom -This - - - is- -an- - open - - - - -access - - - - - -article - - - - - under - - - - - the - - - terms - - - - - of- - the - - - Creative - - - - - - - - - - - - - - - - - - - - -Relevant - - - - - - - conflicts - - - - - - - -of- - interest/financial - - - - - - - - - - - - - - -disclosures: - - - - - - - - - - Nothing - - - - - - -to- - - - - - - Commons Attribution-NonCommercial-NoDerivs License, which permits report. use and distribution in any medium, provided the original work is prop- erly cited, the use is non-commercial and no modifications or adapta- Full financial disclosures and author roles may be found in the online tions are made. version of this article. *Correspondence to: Dr. Matthias Brendel, Department of Nuclear Received: 7 August 2019; Revised: 21 October 2019; Accepted: 23 Medicine, University Hospital of Munich, Marchionistraße 15, 81377 October 2019 Munich, Germany; E-mail: matthias.brendel@med.uni-muenchen.de Published online 16 December 2019 in Wiley Online Library Ms. Huber and Dr. Beyer contributed equally to this work. (wileyonlinelibrary.com). DOI: 10.1002/mds.27945 Movement Disorders, Vol. 35, No. 4, 2020 595 15318257, 2020, 4, Downloaded from https://movementdisorders.onlinelibrary.wiley.com/doi/10.1002/mds.27945 by Readcube (Labtiva Inc.), Wiley Online Library on [30/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License H U B E R E T A L 30 Institut for Stroke and Dementia Research, University of Munich, Munich, Germany 31 Munich Cluster for Systems Neurology (SyNergy), Munich, Germany 32 Department of Nuclear Medicine, University of Bern, Inselspital, Bern, Switzerland 33 School of Psychology and Counselling and IHBI, Queensland University of Technology, Brisbane, Australia 34 Centre for Age-Related Medicine (SESAM), Stavanger University Hospital, Stavanger, Norway 35 Wolfson Centre for Age-Related Diseases, King’s College London, London, United Kingdom A B S T R A C T : Background: Striatal dopamine deficiency hypermetabolism, pronounced in the basal ganglia and and metabolic changes are well-known phenomena in in limbic regions. With increasing dopamine deficiency, dementia with Lewy bodies and can be quantified in vivo metabolic connectivity showed strong deteriorations by 123I-Ioflupane brain single-photon emission computed in distinct brain regions implicated in disease symp- tomography of dopamine transporter and 18F-fluorodesoxy- toms, with greatest disruptions in the basal ganglia and glucose PET. However, the linkage between both bio- limbic system, coincident with the pattern of relative markers is ill-understood. hypermetabolism. Objective: We used the hitherto largest study cohort of Conclusions: Relative glucose hypermetabolism and combined imaging from the European consortium to elu- disturbed metabolic connectivity of limbic and basal cidate the role of both biomarkers in the pathophysiologi- ganglia circuits are metabolic correlates of dopamine cal course of dementia with Lewy bodies. deficiency in dementia with Lewy bodies. Identification Methods: We compared striatal dopamine deficiency of specific metabolic network alterations in patients and glucose metabolism of 84 dementia with Lewy with early dopamine deficiency may serve as an addi- body patients and comparable healthy controls. After tional supporting biomarker for timely diagnosis of normalization of data, we tested their correlation dementia with Lewy bodies. © 2019 The Authors. by region-of-interest–based and voxel-based methods, Movement Disorders published by Wiley Periodicals, controlled for study center, age, sex, education, Inc. on behalf of International Parkinson and Movement and current cognitive impairment. Metabolic connectivity Disorder Society. was analyzed by inter-region coefficients stratified by dopamine deficiency and compared to healthy controls. Key Words: dopamine deficiency; glucose metabolism; Results: There was an inverse relationship between Lewy body dementia; metabolic connectivity; PET striatal dopamine availability and relative glucose imaging Dementia with Lewy bodies (DLB) is the second-most dopaminergic degeneration is detectable by DaT- common type of late-onset degenerative dementia,1,2 SPECT, even though parkinsonian motor symptoms are which is characterized clinically by its core symptoms: fluc- not yet conspicuous to clinical examination.3,11 tuations in cognition and alertness, parkinsonism, visual FDG-PET provides a surrogate measure of neuronal hallucinations, and rapid eye movement (REM) sleep injury, which can support a diagnosis of DLB by reveal- behavior disorder. According to the accepted diagnostic ing region-specific patterns of altered glucose metabo- criteria, DLB should be suspected when onset dementia lism.3,12 Our consortium recently investigated metabolic occurs before or concurrently with parkinsonism.3,4 The patterns and their covariance with clinical core features complex clinical picture of DLB renders its diagnosis chal- in the hitherto largest cohort of DLB patients undergoing lenging, especially at an early disease stage.3,5 For this rea- molecular imaging, in a study within the framework of son, molecular biomarkers, in particular dopamine the European DLB (E-DLB) Consortium.5.Extending transporter imaging by 123I-Ioflupane brain single-photon beyond regional changes in metabolism, network analy- emission computed tomography (DaT-SPECT) and 18F- sis of FDG-PET revealed altered patterns of metabolic fluordeoxyglucose brain PET (FDG-PET), are now connectivity between different brain regions.13 One such accepted tools supporting a diagnosis of DLB.3,6,7 FDG-PET study in DLB patients showed patterns of Nigrostriatal degeneration, as indicated by reduced perturbed metabolic connectivity involving cholinergic striatal DaT-SPECT binding, can distinguish DLB at an and dopaminergic pathways and in association with the early disease stage from other neurodegenerative condi- spreading synucleinopathy occurring in DLB.14 How- tions, especially Alzheimer’s disease (AD).8,9 A previous ever, studies using functional MRI (fMRI) have provided study showed that reduced DaT-SPECT binding in the inconsistent evidence for increasing, decreasing, or posterior putamen provides highly sensitive differential unaltered functional connectivity in different brain diagnosis of DLB.10 An incomplete nigrostriatal regions or functionally related neuronal circuits in DLB 596 Movement Disorders, Vol. 35, No. 4, 2020 15318257, 2020, 4, Downloaded from https://movementdisorders.onlinelibrary.wiley.com/doi/10.1002/mds.27945 by Readcube (Labtiva Inc.), Wiley Online Library on [30/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License M E T A B O L I S M A N D D O P A M I N E R G I C L O S S I N D E M E N T I A W I T H L E W Y B O D I E S patients compared to healthy controls (HCs) and/or AD included in the generation of Z-score values against the patients.15-19 corresponding HC data. We calculated the DaT ratio in Although both DaT-SPECT and FDG-PET are now the bilateral putaminal volumes of interest (VOIs) as well-established procedures for indicative or supportive defined in BRASS model 5, relative to uptake in the diagnosis of DLB, there is only limited documentation bilateral occipital lobe reference region. of the covariance of the two biomarkers. In Parkinson’s FDG-PET preprocessing for all subjects was performed disease (PD) and atypical parkinsonian syndromes, met- in Munich according to procedures established in the pre- abolic network expression and dopamine availability vious E-DLB work.25 In brief, the original FDG-PET were found to provide complementary information DICOM image files were spatially normalized (nonlinear underlying the disease process.20,21 Although there are warping; transient input filter 8 mm3; 16 iterations; fre- several studies investigating the relationships of these quency cutoff 3; regularization 1.0; no thresholding) with two biomarkers’ relationship in PD,21,22 corresponding PMOD (V3.5; PMOD Technologies, Basel, Switzerland) results are available in only in a few DLB cases so to an FDG-PET template. Individual images were inten- far.20,23 Therefore, we undertook to evaluate the rela- sity normalized to their global mean and then smoothed tionship between individual dopamine deficiency and using a Gaussian filter (8 mm3) to obtain maps of FDG altered glucose metabolism in a well-powered, cross- uptake as standardized uptake value ratios (SUVrs) in sectional analysis of our large multicenter E-DLB 77 predefined cortical and subcortical gray matter VOIs cohort. First, we correlated individual dopamine defi- of the Hammers atlas27 after exclusion of the ventricular ciency as indicated by DaT-SPECT with striatal glucose system and the corpus callosum. Brain regions other than metabolism measured by FDG-PET. We then under- insula, cerebellum, and pons were assigned to one of the took regional and voxel-wise analyses to investigate the following six structural or functional compartments: association between the two biomarkers throughout the frontal cortex, parietal cortex, temporal cortex, occipital brain. Finally, we calculated metabolic connectivity as a cortex, limbic system, and basal ganglia. The putamen function of the extent of dopamine deficiency. uptake represented the extended striatum, given that VOI placement in the caudate nucleus tended to impinge upon the cerebral ventricles. Materials and Methods To evaluate potential quantification bias of relative Research Project, Conception, Design, and glucose metabolism increases arising from the normali- Patient Selection zation method, we also reprocessed all data by scaling to cerebellar gray matter values and a cluster-based We took advantage of the framework of the E-DLB normalization. Corresponding results are presented consortium that had agreed to share clinical and imaging online (see Supporting Information Fig. 1). The refer- data from patients with DLB,24 including recently publi- ence cluster was determined by similarity analysis shed findings of the project’s FDG-PET arm.25 From the (voxels ≤2% deviation) between DLB patients and 189 patients with an available anonymized brain FDG- HCs.28 Values deriving from global mean scaling were PET scan from nine centers, we assembled the 89 patients used for further analyses. Data were visually checked with additional DaT-SPECT images in their original for image quality after each process step. The mean Digital Imaging and Communications in Medicine time interval between FDG-PET and DaT-SPECT imag- (DICOM) format. Five patients were excluded because of ing was +3.0  4.8 months. Disease duration was the insufficient demographic data (see a previous work25), elapsed time between the first symptoms of the patients resulting in 84 included subjects. Age-matched DaT- and the date of scanning. SPECT images of 37 historical HCs from a previously published, multicenter study26 and FDG-PET images of 28 HCs imaged in Genova and Munich (14 each) served as control material. Statistical Analysis Local institutional ethics committee approvals for the Statistical tests were performed using SPSS (V25.0; retrospective analyses were available for all centers, IBM Corp., Chicago, IL) and statistical parametric including the transfer of imaging data. Patients gave mapping (SPM; V12; Functional Imaging Laboratory, informed written consent for the imaging procedure The Wellcome Trust Centre in the Institute of Neurol- and radiopharmaceutical application. ogy, University College of London, London, UK) run- ning in Matlab (version R2016a; The MathWorks, DaT-SPECT and FDG-PET Procedure and Inc., Natick, MA). We tested normality of data distri- Preprocessing bution for DaT ratios and all regional FDG-PET SUVr We compiled the DaT-SPECT DICOM files and eval- measurements with the Kolmogorov-Smirnov test. Puta- uated them with Hermes HybridBRASS using the stan- men DaT ratios and regional FDG-PET SUVrs in the dard settings for this analysis. Correction for age was putamen were compared between the DLB patient and Movement Disorders, Vol. 35, No. 4, 2020 597 15318257, 2020, 4, Downloaded from https://movementdisorders.onlinelibrary.wiley.com/doi/10.1002/mds.27945 by Readcube (Labtiva Inc.), Wiley Online Library on [30/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License H U B E R E T A L FIG. 1. VOI and voxel-based associations of DaT availability and relative glucose metabolism. (A) Put_L/R, left/right putamen. (B) FDG SUVr, relative glucose metabolism. (C) Different symbols indicate different centers. (D) Clusters of negative/positive (red/ blue) associations (predictor DaT availability, outcome variable and relative glucose metabolism). (E) Clusters of relative hypo-/hypermetabolism (blue/red; DLB vs. HC). (F) Quantitative comparison of associations between DaT availability and relative glucose metabolism (black bars) and the disease-related pattern of DLB patients when compared to HCs (white bars) for different brain regions. The average values (SD [standard deviation]) of regional T-scores deriving from SPM are given for each brain region. *P < 0.05; **P < 0.01, error bars indicate SDs. Brain regions in D/E are masked by a gray matter atlas. [Color figure can be viewed at wileyonlinelibrary.com] 598 Movement Disorders, Vol. 35, No. 4, 2020 15318257, 2020, 4, Downloaded from https://movementdisorders.onlinelibrary.wiley.com/doi/10.1002/mds.27945 by Readcube (Labtiva Inc.), Wiley Online Library on [30/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License M E T A B O L I S M A N D D O P A M I N E R G I C L O S S I N D E M E N T I A W I T H L E W Y B O D I E S HC groups by an unpaired Student’s t test. A signifi- subgroups. Fisher’s R to Z transformation was performed cance level of P < 0.05 was applied in all analyses. for all values to enhance normal distribution. A signifi- cance level of P < 0.05 after Bonferroni correction was Correlation Analyses applied in all analyses. Furthermore, the regional regres- Pearson’s coefficient of correlation (R) for the two sion coefficients between DaT Z-Score and FDG-PET biomarkers in the putamen was calculated after SUVr in putamen were correlated with mean loss of con- adjusting for center, subject age, sex, education level, nectivity in the corresponding investigated brain regions and disease duration. A subgroup of 70 DLB patients defined as the difference (delta) between DLB and HCs, also allowed correction for current cognitive impair- calculated by subtracting values of each inter-region ment as assessed by the Mini-Mental State Examination coefficient. (MMSE) score. Associations between each subject’s DaT Z-score and FDG-PET SUVr in all predefined brain regions were characterized by applying linear Results regression analyses. All values were adjusted for age Associations Between Dopamine Deficiency (n = 84), sex (n = 84), education (n = 84), disease dura- and Relative FDG Uptake tion (n = 84), and MMSE (n = 70) by linear regression Demographics and disease parameters of the DLB before the analysis. We applied a strict Bonferroni cor- cohort and our historical HC groups are presented in rection to account for multiple comparisons of the Table 1. Our DLB cohort revealed a strongly reduced 77 brain regions. dopamine transporter availability in bilateral putamen (–48%, P < 0.001, t test; Fig. 1A) and significantly higher Voxel-wise Analyses relative glucose metabolism rates (+7.5%, P < 0.001, t test; Voxel-wise statistical analyses were performed using Fig. 1B) in the bilateral putamen when compared to the SPM12. A regression analysis was performed using age-matched HC group. Importantly, there was a negative putamen DaT Z-Scores as predictor and voxel-wise association between DaT Z-scores and FDG-PET SUVr in FDG-PET SUVr as outcome variable, with imaging cen- the putamen after controlling for imaging center, subject ter, subject age, sex, and education level as covariates age, sex, and education (R = –0.269; P = 0.016, partial (SPM threshold P < 0.05, uncorrected [unc.]). Addition- correlation; Fig. 1C). The two imaging biomarkers were ally, we made a voxel-wise contrast of FDG-PET SUVr still significantly associated after additional controlling images of DLB patients versus HCs using an unpaired for disease duration (R = –0.253; P = 0.030, partial t test and compared regional t-scores between the correlation). Furthermore, this association remained regression analysis and the contrast against HCs (SPM stable in the 70 cases where MMSE score was available threshold P < 0.005, unc.). Binarized SPM maps were to control for current cognitive impairment, together tested for similarity by calculation of Dice coefficients with the usual adjustment for age, sex, and education as previously described.29 (R = –0.318; P = 0.009, partial correlation), compared to only adjusting for age, sex, and education in that sub- Metabolic Connectivity group (R = –0.322; P = 0.008, partial correlation). We We assessed metabolic connectivity from inter-regional coefficients of FDG-PET SUVr for DLB patients at differ- TABLE 1. Demographics and composition of the study ent stages of dopamine deficiency and for the HC group, collective as initially described by Horwitz and colleagues.30 For HC HC this purpose, regional FDG-PET values of all 77 brain DLB (FDG-PET) (DaT-SPECT) regions were extracted after normalization to global mean, and an inter-region correlation matrix of 77 × 77 N 84 28 37 Age (y) 72.6  7.0 73.2  7.6 70.1  6.0 Pearson’s correlation coefficients was created for each Sex (♂/♀) 47♂ 37♀ 17♂ 11♀ 21♂ 16♀ analyzed group. MMSE (0–30) 22.6  4.3 The DLB cohort was stratified by tertiles of 28 subjects Education (y) 12.1  3.7 each with severe, intermediate, and mild dopamine defi- Disease duration (y) 2.7  2.0 ciency to the DaT-SPECT Z-Score (age/sex not statistically Parkinsonism 82% Visual hallucinations 65% different). Thus, we performed the connectivity analysis in Fluctuating cognition 73% these three subgroups and a group of 28 age-matched REM sleep behavior 49% HCs to avoid statistical biases attributed to differing sam- disorder ple sizes. Single inter-region coefficients of different brain Possible DLBa 26% areas were compared between the resulting four groups by Probable DLBa 74% analysis of variance, including Bonferroni correction for a McKeith criteria of 2017.3 the number of coefficients studied and the number of N, number of subjects; y, years. Movement Disorders, Vol. 35, No. 4, 2020 599 15318257, 2020, 4, Downloaded from https://movementdisorders.onlinelibrary.wiley.com/doi/10.1002/mds.27945 by Readcube (Labtiva Inc.), Wiley Online Library on [30/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License H U B E R E T A L FIG. 2. Region-based associations of dopamine transporter availability and relative glucose metabolism. The range of FDG-SUVr values of healthy controls  standard deviation indicated in gray. Significant associations (after Bonferroni correction) of 6 of 77 tested regions are indicated with an asterisk (“*”).DaT, putaminal dopamine transporter. R, right. L, left. FDG-SUVr, relative glucose metabolism. [Color figure can be viewed at wileyonlinelibrary.com] could not control for study center in combination with dopamine degeneration (Fig. 1F). There were only MMSE because of the heterogeneous MMSE distribu- minor overlaps between negative correlation clusters tion across study centers. and reduced relative glucose metabolism in DLB (Dice Compared to HCs, the DLB-related pattern (DLBRP) similarity: 3%) and between positive correlation clus- in FDG-PET showed a relatively reduced metabolism in ters and increased relative glucose metabolism in DLB the parietal, occipital, and, to a lesser extent, frontal (Dice similarity: 8%). cortices and a relative hypermetabolism in the basal Next, we applied region-based linear regression of ganglia, parts of the limbic system, the motor cortices, FDG uptake as a function of DaT Z-Scores for all and the cerebellum (Fig. 1E). 77 brain regions with correction for multiple compari- The voxel-wise regression analysis revealed clusters sons (Fig. 2) after adjustment for age, sex, education, showing a negative correlation between both bio- disease duration, and current cognitive function. Nota- markers, which was most conspicuous in the basal bly, only negative significant associations were ganglia and limbic system, that is, the putamen, thala- observed: in the right nucleus accumbens (R = –0.380; mus, SN, and anterior and posterior cingulate gyrus F2,81 = 13.9; P = 0.028), the right thalamus (R = – (Fig. 1D, red). Clusters with positive associations 0.510; F2,81 = 28.9; P = 0.0001), the bilateral anterior were observed in parietal and occipital cortices as well cingulate gyrus (R = –0.402/–0.389; F2,81 = 15.8/14.6; as in the cerebellum (Fig. 1D, blue). Negative correla- P = 0.012/0.020), the right posterior cingulate gyrus tion clusters in the basal ganglia and limbic system (R = –0.389; F2,81 = 14.6; P = 0.020), and the right sub- overlapped with regions of general increases within callosal area (R = –0.367; F2,81 = 12.8; P = 0.046; the DLBRP (Dice similarity: 24%; Fig. 1E,F). The Fig. 2). The other brain regions did not show significant positive correlation clusters in the occipital cortex correlations. The initially observed direct association in overlapped with regions of generally reduced relative the putamen did not survive correction for multiple glucose metabolism within the DLBRP (Dice similar- comparisons (Fig. 2). ity: 41%; Fig. 1E,F). Relative FDG uptake in the pari- etal cortex showed only a weak positive correlation with dopamine deficiency, but this cortical region rev- Metabolic Connectivity at Different Stages of ealed the strongest decrease of relative glucose metab- Dopamine Deficiency in DLB olism within the DLBRP (Fig. 1F). Relative increases Metabolic connectivity was analyzed in three sub- of glucose metabolism in temporal regions were groups of DLB patients of the same size (28 subjects accompanied by weak correlations with nigrostriatal each) stratified according to their degree of dopamine 600 Movement Disorders, Vol. 35, No. 4, 2020 15318257, 2020, 4, Downloaded from https://movementdisorders.onlinelibrary.wiley.com/doi/10.1002/mds.27945 by Readcube (Labtiva Inc.), Wiley Online Library on [30/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License M E T A B O L I S M A N D D O P A M I N E R G I C L O S S I N D E M E N T I A W I T H L E W Y B O D I E S FIG. 3. Metabolic connectivity at different stages of dopaminergic loss, as indexed by DaT SPECT. (A–D) Single boxes indicate inter-regional Z’. Regions with significant differences between groups are highlighted with black, black-dotted, and white lines. (E–I) DD = dopamine depletion to DaT SPECT. Error bars indicate standard deviations. FRO, frontal cortex; TMP, temporal cortex; PAR, parietal cortex; OCC, occipital cortex; I, insula; BAG, basal ganglia; C, cerebellum and brainstem; LIM, limbic structures. [Color figure can be viewed at wileyonlinelibrary.com] deficiency (mild, mean DaT Z-Score of –1.6  0.9; that in a size- and age-matched HC group (28 subjects) intermediate, mean DaT Z-Score of –3.5  0.3; severe, with presumably intact nigrostriatal dopamine innerva- mean DaT Z-Score of –4.5  0.5) and compared to tion (Fig. 3A–D). Movement Disorders, Vol. 35, No. 4, 2020 601 15318257, 2020, 4, Downloaded from https://movementdisorders.onlinelibrary.wiley.com/doi/10.1002/mds.27945 by Readcube (Labtiva Inc.), Wiley Online Library on [30/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License H U B E R E T A L Discussion We present the first large-scale investigation of the rela- tionship between dopamine deficiency in the bilateral putamen and global perturbations of relative glucose metabolism in DLB patients. Our data provide evidence for an association between dopaminergic deficiency and increased relative glucose metabolism pronounced in the basal ganglia and limbic system, independent of disease duration, current cognitive impairment, and other com- mon covariates. Metabolic connectivity in these same brain areas increases slightly at an early stage of dopamine deficiency, but deteriorates strongly when nigrostriatal degeneration is of intermediate or severe magnitude. An extensive body of research documents the PD-related pattern (PDRP) of metabolism in relation to clinical and dopaminergic measures.20,21 There was a significant inverse relationship between dopamine transporter avail- ability in the striatum and PDRP expression, whereas nei- ther extent of dopamine deficiency nor PDRP expression correlated strongly with disease duration or severity.20 In FIG. 4. Association between relative regional hypermetabolism and loss of connectivity. DaT, putaminal dopamine transporter. Regression coefficient our DLB cohort, region-based and voxel-wise correlation (association analysis of DaT availability and relative glucose metabolism; analyses also showed strong associations between dopa- Fig. 2). Change of metabolic connectivity (Δ mean Z’ vs. healthy controls; Fig. 3). [Color figure can be viewed at wileyonlinelibrary.com] mine deficiency and the disease-specific metabolic pattern of DLB. Notably, we found a significant correlation between dopamine deficiency in the putamen and increas- Strikingly, the strongest alterations of metabolic ing relative glucose metabolism throughout the bilateral connectivity in comparison to HCs were observed striatum, resulting in an overall striatal hypermetabolism within the limbic circuits and basal ganglia (see black of DLB subjects compared to HCs. Until now, changes of boxes in Fig. 3A–D and 3E,F) and between the limbic striatal glucose metabolism have not been evaluated in circuit and basal ganglia (see black dotted boxes in detail with respect to the course of DLB, although the stri- Fig. 3A–D and 3G). The metabolic connectivity was atum is certainly vulnerable to DLB pathology.8 In PD, it increased in the DLB group with mild dopamine defi- has been shown that the neuronal firing rate and syn- ciency compared to age-matched HCs, but connectiv- chrony of neurons in the dorsal striatum is altered along ity then fell significantly lower for DLB stages of the course of disease progression, in concert with continu- intermediate or severe dopamine deficiency (Fig. 3E–G). ing dopamine deficiency.31 This relationship may arise We also observed a significant decrease of metabolic because of effects of a pacemaker center in the basal gang- connectivity between parieto-occipital brain areas and lia that compensates dopaminergic loss with increased the limbic system/basal ganglia, which was enhanced neuronal firing and consequently increased metabolic with increasing dopamine deficiency (see white boxes activity.32 Thus, our current findings in DLB could also in Fig. 3A–D and 3H,I). Other brain areas showed no reflect a compensatory or disinhibition mechanism significant differences in metabolic connectivity. whereby metabolism increases in the partially dopamine- Metabolic connectivity was significantly lower for depleted striatum. Importantly, the association between regions with a higher association between dopamine loss of dopamine innervation and relatively increasing glu- deficiency and relative glucose metabolism (R = –0.665; cose metabolism in the putamen remained after control- P < 0.01; Fig. 4). Single regions clustered along their ling for disease duration and current cognitive function, as defined anatomical or functional compartments when indexed by the MMSE score. plotting regression coefficients deriving from the associ- Compared to the PDRP, the DLBRP measured in our ation between dopamine deficiency and relative glucose study showed additional regions of relative hypometabolism metabolism together with connectivity changes. In sum- in the occipital cortices and, even more important, addi- mary, we observed a clear linkage between the relative tional regions of relative hypermetabolism in the limbic sys- hypermetabolism associated with dopaminergic loss tem. In comparison to PD, patients with DLB present with and decreased metabolic connectivity at the regional additional core symptoms like visual hallucinations and cog- level of DLB patients. nitive impairment arising from the more pronounced 602 Movement Disorders, Vol. 35, No. 4, 2020 15318257, 2020, 4, Downloaded from https://movementdisorders.onlinelibrary.wiley.com/doi/10.1002/mds.27945 by Readcube (Labtiva Inc.), Wiley Online Library on [30/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License M E T A B O L I S M A N D D O P A M I N E R G I C L O S S I N D E M E N T I A W I T H L E W Y B O D I E S cortical involvement. As we have observed, additional brain compensatory mechanism, whereas impaired metabolic regions, such as the limbic system, also showed relative glu- connectivity at later disease stages could reflect a break- cose hypermetabolism in the contrast between DLB patients down or decompensation of the increasingly disturbed and HCs and, furthermore, revealed a strong negative corre- neuronal activity. Our findings are supported by one lation with the extent of dopaminergic loss in the putamen fMRI investigation, which found decreased functional (Fig. 1F). We speculate that increasing relative limbic glucose connectivity in limbic and basal ganglia clusters of tem- metabolism arises secondary to impaired dopaminergic sig- poral and frontoparietal networks of a group of naling in the basal ganglia, perhaps attributable to disinhibi- patients with advanced DLB.17 An analogous compen- tion or as a compensatory response to decreasing signal satory recruitment hypothesis has already been pro- input. This finding is of particular interest given that it may posed to occur in the course of AD.34 In this scenario, also harbor the molecular basis for the cingulate island sign connectivity initially increases because new brain areas in DLB, that is, the relative sparing of mid-posterior cingu- are recruited, and impaired regions become hyperactive late cortex metabolism.33 Some areas with a negative corre- to compensate for functional loss in a process pre- lation between putaminal DaT-SPECT and voxel-wise senting as relative hypermetabolism to FDG-PET. The FDG-PET were observed in the white matter and near the compensation process cannot be maintained when neu- ventricles. This probably spurious finding could be related rodegeneration proceeds past some point, whereupon to the presence of greater AD-like and vascular pathology in decreasing metabolic connectivity is declared at this those DLB subjects with relatively preserved dopaminergic later disease stage. The seemingly dynamic changes of innervation, resulting in proportionately greater atrophy metabolic connectivity involving the limbic system and white matter disease. Future imaging studies need to might explain why relative FDG uptake did only partly draw attention toward the impact of various existing differ in the voxel-wise analysis for the entire DLB copathologies in DLB on our observed association between group as compared to HCs (see Fig. 1). dopaminergic loss in striatum and metabolic function in the Among the limitations of the study, we note that brain. DLB diagnoses were obtained in different clinical set- To get a deeper insight into potential pathomechanisms tings and without histopathological verification. Fur- resulting in perturbed glucose metabolism in DLB sub- thermore, there might occur some selection bias into jects, we investigated metabolic connectivity in FDG-PET the direction of a more complex cohort of DLB patients by inter-region coefficient methods. Functional connectiv- in comparison to the general average in populations, ity in DLB subjects has already been investigated in several given that patients evaluated by both imaging bio- fMRI studies.15-19 Most of these fMRI studies investigated markers had potentially a more conflicting clinical the discrepancies between DLB subjects and HC and/or examination. Potential confounders, such as different AD subjects, while focusing on particular brain regions cameras or nonstandardized enrollment of patients known to be affected in the respective diseases. As we have (clinical evaluation and examination), were minimized demonstrated in the present study, some of the discrepant by inclusion of the imaging center as a covariate. Filter- findings of those fMRI studies may arise from reporting ing of FDG-PET data was used to correct for possible connectivity alterations without adjustment for the stage spatial mismatch between subjects. Nonetheless, we of dopaminergic loss of the included DLB subjects. In cannot fully exclude the possibility of some remaining our cohort, the combined analysis of dopamine deficiency effects attributed to the multicenter design of this cross- and relative glucose metabolism offers the opportunity sectional study. to investigate metabolic connectivity as a function of The use of a global mean normalization may raise con- dopaminergic loss. Stratified as mild/intermediate/severe cerns about a potential artificial relative hypermetabolism dopamine degeneration, we found seemingly increased attributed to a lower global glucose metabolism in subjects metabolic connectivity in limbic and basal ganglia regions affected by DLB (i.e., causing a low denominator).35,36 at the early stage of dopaminergic loss compared to that Although some research indicates absolute declines in cor- in HCs (Fig. 3). Enhanced metabolic connectivity has tical glucose consumption in patients with PD,37 others already been reported between the right posterior cingu- report no significant difference between global glucose late cortex and other brain regions (inter alia the limbic metabolism in PD patients compared to HCs, despite a system) in a previous study of DLB subjects.16 With focal increase of striatal metabolism.38 Moreover, another increasing nigrostriatal dopamine degeneration, the meta- study found a correlation between the changes of glucose bolic connections involving the basal ganglia and limbic metabolism in subcortical brain regions and motor func- system decreased and the metabolic connectivity of those tion deterioration, which speaks against the interpretation brain regions was significantly lower than for DLB sub- that increased values are merely artefacts of the normaliza- jects with mild dopamine innervation loss in the putamen. tion process.39 To strengthen this argument, we tested for This finding supports the conjecture that relative the robustness of our findings of increased striatal glucose hypermetabolism in the basal ganglia and limbic system metabolism with normalization to other reference regions in early DLB disease stages represents a disinhibition or (see Supporting Information Fig. 1). Although there were Movement Disorders, Vol. 35, No. 4, 2020 603 15318257, 2020, 4, Downloaded from https://movementdisorders.onlinelibrary.wiley.com/doi/10.1002/mds.27945 by Readcube (Labtiva Inc.), Wiley Online Library on [30/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License H U B E R E T A L varying degrees of relative increase in the striatum, corre- 2. Walker Z, Possin KL, Boeve BF, Aarsland D. Lewy body dementias. Lancet 2015;386:1683–1697. lations of relative glucose metabolism with reduced DaT 3. McKeith IG, Boeve BF, Dickson DW, et al. Diagnosis and manage- availability in the putamen remained stable. Thus, some ment of dementia with Lewy bodies: fourth consensus report of the areas with relatively increased glucose metabolism, such DLB Consortium. Neurology 2017;89:88–100. as the white matter of the bilateral frontal and parietal cor- 4. Walker Z, Moreno E, Thomas A, et al. Evolution of clinical features tices, which might have been artifacts of the global mean in possible DLB depending on FP-CIT SPECT result. Neurology 2016;87:1045–1051. normalization, did not significantly influence the present 5. Morbelli S, Chincarini A, Brendel M, et al. Metabolic patterns key results. across core features in dementia with lewy bodies. Ann Neurol Our inter-region coefficient method represents a simpli- 2019;85:715–725. fied model to assess metabolic or functional connectivity, 6. Bauckneht M, Arnaldi D, Nobili F, Aarsland D, Morbelli S. New which has previously revealed inter-regional correlations tracers and new perspectives for molecular imaging in Lewy body diseases. Curr Med Chem 2018;25:3105–3130. in an fMRI study.40 However, our primary focus was to search for differences in metabolic connectivity as a func- 7. McKeith I, O’Brien J, Walker Z, et al. Sensitivity and specificity of dopamine transporter imaging with 123I-FP-CIT SPECT in dementia tion of the stage of putaminal dopaminergic loss in DLB. with Lewy bodies: a phase III, multicentre study. Lancet Neurol Therefore, we argue that a simple model is well suited to 2007;6:305–313. the task at hand. We note that FDG-PET has inherently 8. Jellinger KA, Attems J. Does striatal pathology distinguish Parkinson disease with dementia and dementia with Lewy bodies? Acta Neuro- lower spatial resolution compared to fMRI studies, but pathol 2006;112:253–260. presume that functional connectivity operates for anatom- 9. O’Brien JT, Colloby S, Fenwick J, et al. Dopamine transporter loss ical regions of the scale of our 77 atlas-based brain regions. visualized with FP-CIT SPECT in the differential diagnosis of FDG-PET has shown comprehensible results regarding dementia with Lewy bodies. Arch Neurol 2004;61:919–925. the sufficient spatial resolution for metabolic connectivity 10. Walker Z, Jaros E, Walker RW, et al. Dementia with Lewy bodies: a comparison of clinical diagnosis, FP-CIT single photon emission analyses in AD subjects.41 We note that the MMSE score computed tomography imaging and autopsy. J Neurol Neurosurg certainly does not represent all aspects of disease severity Psychiatry 2007;78:1176–1181. in DLB. Unfortunately, to our knowledge, there is no 11. Nicastro N, Garibotto V, Allali G, Assal F, Burkhard PR. Added widely accepted score to measure overall clinical severity value of combined semi-quantitative and visual [123I]FP-CIT SPECT analyses for the diagnosis of dementia with Lewy bodies. in DLB. Therefore, MMSE was the only feasible, stan- Clin Nucl Med 2017;42:e96–e102. dardized index for use in this multicenter study. 12. Nobili F, Arbizu J, Bouwman F, et al. European Association of Overall, we present the, so far, largest combined anal- Nuclear Medicine and European Academy of Neurology recommen- ysis of dopamine deficiency to DaT-SPECT imaging and dations for the use of brain (18) F-fluorodeoxyglucose positron emission tomography in neurodegenerative cognitive impairment widespread cerebrometabolic changes to FDG-PET in and dementia: Delphi consensus. Eur J Neurol 2018;25:1201–1217. the same DLB subjects. Our data indicate a strong inter- 13. Morbelli S, Perneczky R, Drzezga A, et al. Metabolic networks relation, with a pronounced association of dopamine underlying cognitive reserve in prodromal Alzheimer disease: a deficiency with increasing relative glucose metabolism in European Alzheimer disease consortium project. J Nucl Med 2013; 54:894–902. the basal ganglia and limbic system. Furthermore, a 14. Caminiti SP, Tettamanti M, Sala A, et al. Metabolic connectomics more advanced stage of dopaminergic loss manifested in targeting brain pathology in dementia with Lewy bodies. J Cereb decreased metabolic connectivity within brain regions of Blood Flow Metab 2017;37:1311–1325. the disease-related metabolic pattern of DLB, pro- 15. Galvin JE, Price JL, Yan Z, Morris JC, Sheline YI. Resting bold nounced in the basal ganglia and limbic system. These fMRI differentiates dementia with Lewy bodies vs Alzheimer disease. Neurology 2011;76:1797–1803. findings clearly show that disturbed brain metabolism in 16. Kenny ER, Blamire AM, Firbank MJ, O’Brien JT. Functional con- particular regions is linked to extent of dopaminergic nectivity in cortical regions in dementia with Lewy bodies and loss in DLB. As already proposed for PD and other atyp- Alzheimer’s disease. Brain 2012;135(Pt 2):569–581. ical parkinsonian syndromes, the identification of meta- 17. Peraza LR, Kaiser M, Firbank M, et al. fMRI resting state networks bolic network alterations at early stages of dopaminergic and their association with cognitive fluctuations in dementia with Lewy bodies. Neuroimage Clin 2014;4:558–565. loss, preceding onset of motor symptoms, may serve as a 18. Peraza LR, Colloby SJ, Deboys L, O’Brien JT, Kaiser M, Taylor JP. supporting biomarker for diagnosis of DLB. Regional functional synchronizations in dementia with Lewy bodies and Alzheimer’s disease. Int Psychogeriatr 2016;28:1143–1151. Acknowledgments: We are grateful to the European Dementia with 19. Schumacher J, Peraza LR, Firbank M, et al. Functional connectivity Lewy Bodies consortium for this inspiring collaboration and intellectual in dementia with Lewy bodies: a within- and between-network anal- exchange. We thank radiographers and technicians at all study centers for ysis. Hum Brain Mapp 2018;39:1118–1129. their invaluable support in data acquisition. 20. Ko JH, Lee CS, Eidelberg D. Metabolic network expression in par- kinsonism: clinical and dopaminergic correlations. J Cereb Blood Flow Metab 2017;37:683–693. 21. Schindlbeck KA, Eidelberg D. Network imaging biomarkers: insights References and clinical applications in Parkinson’s disease. Lancet Neurol 2018;17:629–640. 1. Barker WW, Luis CA, Kashuba A, et al. Relative frequencies of Alzheimer disease, Lewy body, vascular and frontotemporal demen- 22. Niethammer M, Tang CC, Ma Y, et al. Parkinson’s disease cognitive tia, and hippocampal sclerosis in the State of Florida Brain Bank. network correlates with caudate dopamine. Neuroimage 2013;78: Alzheimer Dis Assoc Disord 2002;16:203–212. 204–209. 604 Movement Disorders, Vol. 35, No. 4, 2020 15318257, 2020, 4, Downloaded from https://movementdisorders.onlinelibrary.wiley.com/doi/10.1002/mds.27945 by Readcube (Labtiva Inc.), Wiley Online Library on [30/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License M E T A B O L I S M A N D D O P A M I N E R G I C L O S S I N D E M E N T I A W I T H L E W Y B O D I E S 23. Massa F, Arnaldi D, De Cesari F, et al. Neuroimaging findings and impairment in Alzheimer’s disease vs. dementia with Lewy bodies. clinical trajectories of Lewy body disease in patients with MCI. Neu- J Neuroimaging 2019;29:712–720. robiol Aging 2019;76:9–17. 34. Celone KA, Calhoun VD, Dickerson BC, et al. Alterations in memory 24. Biundo R, Weis L, Bostantjopoulou S, et al. MMSE and MoCA in networks in mild cognitive impairment and Alzheimer’s disease: an Parkinson’s disease and dementia with Lewy bodies: a multicenter independent component analysis. J Neurosci 2006;26:10222–10231. 1-year follow-up study. J Neural Transm (Vienna) 2016;123: 35. Borghammer P, Cumming P, Aanerud J, Gjedde A. Artefactual 431–438. subcortical hyperperfusion in PET studies normalized to global 25. Morbelli S, Chincarini A, Brendel M, et al. Metabolic patterns mean: lessons from Parkinson’s disease. Neuroimage 2009;45: across core features in dementia with Lewy bodies. Ann Neurol 249–257. 2019;85:715–725. 36. Borghammer P, Jonsdottir KY, Cumming P, et al. Normalization in 26. Albert NL, Unterrainer M, Diemling M, et al. Implementation of the PET group comparison studies—the importance of a valid reference European multicentre database of healthy controls for [(123)I]FP- region. Neuroimage 2008;40:529–540. CIT SPECT increases diagnostic accuracy in patients with clinically 37. Borghammer P, Chakravarty M, Jonsdottir KY, et al. Cortical uncertain parkinsonian syndromes. Eur J Nucl Med Mol Imaging hypometabolism and hypoperfusion in Parkinson’s disease is exten- 2016;43:1315–1322. sive: probably even at early disease stages. Brain Struct Funct 2010; 27. Hammers A, Allom R, Koepp MJ, et al. Three-dimensional maxi- 214:303–317. mum probability atlas of the human brain, with particular reference 38. Ma Y, Tang C, Moeller JR, Eidelberg D. Abnormal regional brain to the temporal lobe. Hum Brain Mapp 2003;19:224–247. function in Parkinson’s disease: truth or fiction? NeuroImage 2009; 45:260–266. 28. Yakushev I, Hammers A, Fellgiebel A, et al. SPM-based count nor- malization provides excellent discrimination of mild Alzheimer’s dis- 39. Lozza C, Marie RM, Baron JC. The metabolic substrates of ease and amnestic mild cognitive impairment from healthy aging. bradykinesia and tremor in uncomplicated Parkinson’s disease. Neuroimage 2009;44:43–50. NeuroImage 2002;17:688–699. 29. Brendel M, Kleinberger G, Probst F, et al. Increase of TREM2 during 40. Gardner RC, Boxer AL, Trujillo A, et al. Intrinsic connectivity net- aging of an Alzheimer’s disease mouse model is paralleled by micro- work disruption in progressive supranuclear palsy. Ann Neurol glial activation and amyloidosis. Front Aging Neurosci 2017;9:8. 2013;73:603–616. 30. Horwitz B, Duara R, Rapoport SI. Intercorrelations of glucose meta- 41. Morbelli S, Drzezga A, Perneczky R, et al. Resting metabolic con- bolic rates between brain regions: application to healthy males in a nectivity in prodromal Alzheimer’s disease. A European Alzheimer state of reduced sensory input. J Cereb Blood Flow Metab 1984;4: Disease Consortium (EADC) project. Neurobiol Aging 2012;33: 484–499. 2533–2550. 31. Burkhardt JM, Jin X, Costa RM. Dissociable effects of dopamine on neuronal firing rate and synchrony in the dorsal striatum. Front Supporting Data Integr Neurosci 2009;3:28. 32. Plenz D, Kital ST. A basal ganglia pacemaker formed by the sub- Additional Supporting Information may be found in thalamic nucleus and external globus pallidus. Nature 1999;400: 677–682. the online version of this article at the publisher’s 33. Chiba Y, Fujishiro H, Iseki E, Kasanuki K, Sato K. The cingulate web-site. island sign on FDG-PET vs. IMP-SPECT to assess mild cognitive Movement Disorders, Vol. 35, No. 4, 2020 605